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相关概念视频

Magnetic Resonance Imaging01:24

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Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...
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Double Resonance Techniques: Overview01:12

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Double resonance techniques in Nuclear Magnetic Resonance (NMR) spectroscopy involve the simultaneous application of two different frequencies or radiofrequency pulses to manipulate and observe two distinct nuclear spins. One important application of double resonance is spin decoupling, which selectively suppresses coupling with one type of nucleus while observing the NMR signal from another nucleus, simplifying the spectrum and enhancing resolution.
Spin decoupling is usually achieved by...
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Imaging Studies IV: Magnetic Resonance Imaging

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Introduction:Magnetic Resonance Imaging, or MRI, can include a specialized imaging technique of the urinary system known as Magnetic Resonance Urography (MRU). This radiation-free technique uses strong magnetic fields and radio waves to produce detailed images with the help of a computer. MRU is particularly effective for visualizing fluid-filled structures like the kidneys, ureters, and bladder.Applications of MRI in the Genitourinary SystemKidneys and Ureters: MRI detects tumors, cysts,...
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使用次空间多尺度能量模型 (SS-MUSE) 的加速量化MRI

Yan Chen1, Jyothi Rikhab Chand1, Steven R Kecskemeti2

  • 1University of Virginia.

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概括
此摘要是机器生成的。

这项研究引入了一种加速3D多对照MRI扫描的新方法. 基于多尺度的能源模型 (MuSE) 可以减少扫描时间,同时保持图像质量以更好地区分组织.

关键词:
基于能源的模型没有.多对比的MRI插入和运行部分空间

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科学领域:

  • 医学成像
  • 磁共振成像 (MRI)
  • 计算机成像

背景情况:

  • 多对比的MRI对于组织分化和定量绘制至关重要.
  • 在3DMRI中获得长时间限制同位素分辨率.
  • 深度学习方法面临着大型3D数据集的挑战.

研究的目的:

  • 为了加快3D多对比的MRI采集.
  • 克服深度学习对大规模3D卷的限制.
  • 为了实现高分辨率的3D成像与多重对比.

主要方法:

  • 将基于多尺度的能源模型 (MuSE) 推广为正规化的子空间恢复框架.
  • 在子空间配方中对3D多对比空间因子进行联合规范化.
  • 应用可变分割优化以实现高效的图像恢复.

主要成果:

  • 在恢复3D多对比MRI数据方面证明了计算效率.
  • 能够更快地获得高分辨率的3DMRI扫描.
  • 为加快3DMRI提供了可行的深度学习替代方案.

结论:

  • 一般化的MuSE模型有效地加速了3D多对比度MRI采集.
  • 这种方法解决了3DMRI深度学习的计算和记忆挑战.
  • 该方法可提供高质量的同位素3D核磁共振与多重对比.